Artificial Intelligence in the healthcare of older people

Abstract

Clinical applications of Artifi cial Intelligence (AI) in healthcare are relatively rare. The high expectations in relation to data analysis infl uencing general healthcare have not materialized, with few exceptions, and then predominantly in the fi eld of rare diseases, oncology and pathology, and interpretation of laboratory results. While electronic health records, introduced over the last decade or so in the UK have increased access to medical and treatment histories of patients, diagnoses, medications, treatment plans, immunization dates, allergies, radiology images, laboratory and test results, these have potential for evidence-based tools that providers can use to make decisions about a patient's care, as well as streamline workfl ow. In the following text, we review the advances achieved using machine learning and deep learning technology, as well as robot use and telemedicine in the healthcare of older people. Key points: 1. Artifi cial Intelligence use is extensively explored in prevention, diagnosis, novel drug designs and after-care. 2. AI studies on older adults include a small number of patients and lack reproducibility needed for their wider clinical use in different clinical settings and larger populations. 3. Telemedicine and robot assisted technology are well received by older service users. 4. Ethical concerns need to be resolved prior to wider AI use in routine clinical setting. seem to face a number of barriers in contacting communitybased primary health care, such as limited access, lack of standardized information systems and care pathways [3], all necessary to address their complex health care and social care needs. Indeed, older adults have much higher prevalence of nearly all major chronic and long-term conditions. In addition, they are more likely to succumb to adverse health events, such as a falls or infections, and these can lead to dramatic changes in their physical and mental wellbeing even after an apparently minor incident [4]. However, person and familyfocused care, self-management resources, and successful collaborative practice have been all highlighted as facilitators of good health care provision both by older people and their families [3]. All the above places the importance of diagnosis, monitoring of disease risks and their prevention, as well as management and optimizing of geriatric syndromes in the community for both older people living independenly or in 24h care facilities. In particular, identi ication of health issues/ diagnosis, support/treatment needs evaluation, development

Use of Machine Learning and Artificial Intelligence Methods in Geriatric Mental Health Research Involving Electronic Health Record or Administrative Claims Data: A Systematic Review

“…Nevertheless, supervised machine learning (e.g., random forest) was the most commonly used AI technique according to their review, similar to our findings. Another recent study by Elizabeta et al ( 35 ) reviewed AI in the healthcare of older adults. They did not mention any specific number of studies; instead, they discussed some studies where ML or AI approaches were applied in the medical care of older people and concerns associated with AI use in medicine.…”

A review of upper limb robot assisted therapy techniques and virtual reality applications

“…Another importance for AI in the rehabilitation sector is the ability to create a personalized treatment plan that can be used clinically. According to the review study [14], AI contribution to the medical sector can be in form of; disability evaluation, electrodiagnosis, speeding up clinical trials, and many other potential uses.…”

Machine Learning for Geriatric Clinical Care: Opportunities and Challenges

“…care; 9) telemedicine and robot-related technologies for social communication and rehabilitation of geriatric patients with physical disabilities, Parkinson disease, and chronic hemiparetic gait as well as for post-stroke patients; virtual reality environments to improve anxiety, depression, loneliness, social isolation, gait, posture, and pain; 7) and interpretation of motion parameters and assessment of body composition or physical performance from vision datasets or opportunistic imaging datasets. 9,10) Therefore, health managers and policymakers should pay special attention to ML to improve geriatric clinical care.…”

“…11) Some additional challenges of using ML for geriatric clinical care include ethical concerns such as racial biases, 12) privacy, equity, security, disruption of human communication and data management, and cost of care. 7) One of the major challenges in the implementation of ML for geriatric clinical care is the problem of annotation of real-life emotions. Data related to the expression of emotions are rare and cannot provide a basis for annotation and modeling using fine-grained emotion labels.…”

“…13) Therefore, these challenges remain a major con-cern for the implementation of ML in geriatric clinical care. 7) Overall, the implementation of ML to improve geriatric clinical care is a questionable hypothesis requiring additional evidence. Thus, further research is needed to address the challenges of using ML for geriatric clinical care.…”